In [1]:
import pandas as pd
import geopandas as gpd
import numpy as np
import hvplot.pandas
import holoviews as hv
import panel as pn
from matplotlib import pyplot as plt
from holoviews import opts

pd.options.display.max_columns = 999
np.seterr(invalid="ignore");

Assignment 3¶

This assignment will contain two parts:

  1. Exploring evictions and code violations in Philadelphia
  2. Comparing the NDVI in Philadelphia

Part 1: Exploring Evictions and Code Violations in Philadelphia¶

In this assignment, we'll explore spatial trends evictions in Philadelphia using data from the Eviction Lab and building code violations using data from OpenDataPhilly.

We'll be exploring the idea that evictions can occur as retaliation against renters for reporting code violations. Spatial correlations between evictions and code violations from the City's Licenses and Inspections department can offer some insight into this question.

A couple of interesting background readings:

  • HuffPost article
  • PlanPhilly article

1.1 Explore Eviction Lab Data¶

The Eviction Lab built the first national database for evictions. If you aren't familiar with the project, you can explore their website: https://evictionlab.org/

1.1.1 Read data using geopandas¶

The first step is to read the eviction data by census tract using geopandas. The data for all of Pennsylvania by census tract is available in the data/ folder in a GeoJSON format.

Load the data file "PA-tracts.geojson" using geopandas

Note: If you'd like to see all columns in the data frame, you can increase the max number of columns using pandas display options:

In [2]:
PAtracts = gpd.read_file("PA-tracts.geojson")
PAtracts = PAtracts.to_crs("EPSG:32618")

1.1.2 Explore and trim the data¶

We will need to trim data to Philadelphia only. Take a look at the data dictionary for the descriptions of the various columns in top-level repository folder: eviction_lab_data_dictionary.txt

Note: the column names are shortened — see the end of the above file for the abbreviations. The numbers at the end of the columns indicate the years. For example, e-16 is the number of evictions in 2016.

Take a look at the individual columns and trim to census tracts in Philadelphia. (Hint: Philadelphia is both a city and a county).

In [3]:
condition = (PAtracts['pl'] == "Philadelphia County, Pennsylvania")
phltracts = PAtracts[condition]
In [4]:
phltracts.head()
Out[4]:
GEOID west south east north n pl p-00 pr-00 roh-00 pro-00 mgr-00 mhi-00 mpv-00 rb-00 pw-00 paa-00 ph-00 pai-00 pa-00 pnp-00 pm-00 po-00 ef-00 e-00 er-00 efr-00 lf-00 imputed-00 subbed-00 p-01 pr-01 roh-01 pro-01 mgr-01 mhi-01 mpv-01 rb-01 pw-01 paa-01 ph-01 pai-01 pa-01 pnp-01 pm-01 po-01 ef-01 e-01 er-01 efr-01 lf-01 imputed-01 subbed-01 p-02 pr-02 roh-02 pro-02 mgr-02 mhi-02 mpv-02 rb-02 pw-02 paa-02 ph-02 pai-02 pa-02 pnp-02 pm-02 po-02 ef-02 e-02 er-02 efr-02 lf-02 imputed-02 subbed-02 p-03 pr-03 roh-03 pro-03 mgr-03 mhi-03 mpv-03 rb-03 pw-03 paa-03 ph-03 pai-03 pa-03 pnp-03 pm-03 po-03 ef-03 e-03 er-03 efr-03 lf-03 imputed-03 subbed-03 p-04 pr-04 roh-04 pro-04 mgr-04 mhi-04 mpv-04 rb-04 pw-04 paa-04 ph-04 pai-04 pa-04 pnp-04 pm-04 po-04 ef-04 e-04 er-04 efr-04 lf-04 imputed-04 subbed-04 p-05 pr-05 roh-05 pro-05 mgr-05 mhi-05 mpv-05 rb-05 pw-05 paa-05 ph-05 pai-05 pa-05 pnp-05 pm-05 po-05 ef-05 e-05 er-05 efr-05 lf-05 imputed-05 subbed-05 p-06 pr-06 roh-06 pro-06 mgr-06 mhi-06 mpv-06 rb-06 pw-06 paa-06 ph-06 pai-06 pa-06 pnp-06 pm-06 po-06 ef-06 e-06 er-06 efr-06 lf-06 imputed-06 subbed-06 p-07 pr-07 roh-07 pro-07 mgr-07 mhi-07 mpv-07 rb-07 pw-07 paa-07 ph-07 pai-07 pa-07 pnp-07 pm-07 po-07 ef-07 e-07 er-07 efr-07 lf-07 imputed-07 subbed-07 p-08 pr-08 roh-08 pro-08 mgr-08 mhi-08 mpv-08 rb-08 pw-08 paa-08 ph-08 pai-08 pa-08 pnp-08 pm-08 po-08 ef-08 e-08 er-08 efr-08 lf-08 imputed-08 subbed-08 p-09 pr-09 roh-09 pro-09 mgr-09 mhi-09 mpv-09 rb-09 pw-09 paa-09 ph-09 pai-09 pa-09 pnp-09 pm-09 po-09 ef-09 e-09 er-09 efr-09 lf-09 imputed-09 subbed-09 p-10 pr-10 roh-10 pro-10 mgr-10 mhi-10 mpv-10 rb-10 pw-10 paa-10 ph-10 pai-10 pa-10 pnp-10 pm-10 po-10 ef-10 e-10 er-10 efr-10 lf-10 imputed-10 subbed-10 p-11 pr-11 roh-11 pro-11 mgr-11 mhi-11 mpv-11 rb-11 pw-11 paa-11 ph-11 pai-11 pa-11 pnp-11 pm-11 po-11 ef-11 e-11 er-11 efr-11 lf-11 imputed-11 subbed-11 p-12 pr-12 roh-12 pro-12 mgr-12 mhi-12 mpv-12 rb-12 pw-12 paa-12 ph-12 pai-12 pa-12 pnp-12 pm-12 po-12 ef-12 e-12 er-12 efr-12 lf-12 imputed-12 subbed-12 p-13 pr-13 roh-13 pro-13 mgr-13 mhi-13 mpv-13 rb-13 pw-13 paa-13 ph-13 pai-13 pa-13 pnp-13 pm-13 po-13 ef-13 e-13 er-13 efr-13 lf-13 imputed-13 subbed-13 p-14 pr-14 roh-14 pro-14 mgr-14 mhi-14 mpv-14 rb-14 pw-14 paa-14 ph-14 pai-14 pa-14 pnp-14 pm-14 po-14 ef-14 e-14 er-14 efr-14 lf-14 imputed-14 subbed-14 p-15 pr-15 roh-15 pro-15 mgr-15 mhi-15 mpv-15 rb-15 pw-15 paa-15 ph-15 pai-15 pa-15 pnp-15 pm-15 po-15 ef-15 e-15 er-15 efr-15 lf-15 imputed-15 subbed-15 p-16 pr-16 roh-16 pro-16 mgr-16 mhi-16 mpv-16 rb-16 pw-16 paa-16 ph-16 pai-16 pa-16 pnp-16 pm-16 po-16 ef-16 e-16 er-16 efr-16 lf-16 imputed-16 subbed-16 geometry
435 42101000100 -75.1523 39.9481 -75.1415 39.9569 1 Philadelphia County, Pennsylvania 2646.71 9.26 1347.0 77.12 959.0 48886.0 189700.0 24.5 78.45 12.42 3.47 0.23 3.92 0.00 1.40 0.11 NaN NaN NaN NaN 0.0 0.0 0.0 2646.71 9.26 1360.0 77.12 959.0 48886.0 189700.0 24.5 78.45 12.42 3.47 0.23 3.92 0.00 1.40 0.11 NaN NaN NaN NaN 0.0 0.0 0.0 2646.71 9.26 1374.0 77.12 959.0 48886.0 189700.0 24.5 78.45 12.42 3.47 0.23 3.92 0.00 1.40 0.11 21.0 19.0 1.38 1.53 1.0 0.0 0.0 2646.71 9.26 1388.0 77.12 959.0 48886.0 189700.0 24.5 78.45 12.42 3.47 0.23 3.92 0.00 1.40 0.11 25.0 21.0 1.51 1.80 1.0 0.0 0.0 2646.71 9.26 1401.0 77.12 959.0 48886.0 189700.0 24.5 78.45 12.42 3.47 0.23 3.92 0.00 1.40 0.11 25.0 24.0 1.71 1.78 1.0 0.0 0.0 3310.88 12.11 1415.0 57.97 1357.0 73272.0 332500.0 25.6 83.98 7.24 4.40 0.0 3.08 0.0 0.45 0.84 18.0 15.0 1.06 1.27 1.0 0.0 0.0 3310.88 12.11 1428.0 57.97 1357.0 73272.0 332500.0 25.6 83.98 7.24 4.40 0.0 3.08 0.0 0.45 0.84 13.0 10.0 0.70 0.91 1.0 0.0 0.0 3310.88 12.11 1442.0 57.97 1357.0 73272.0 332500.0 25.6 83.98 7.24 4.40 0.0 3.08 0.0 0.45 0.84 53.0 20.0 1.39 3.68 0.0 0.0 1.0 3310.88 12.11 1456.0 57.97 1357.0 73272.0 332500.0 25.6 83.98 7.24 4.40 0.0 3.08 0.0 0.45 0.84 30.0 17.0 1.17 2.06 0.0 0.0 1.0 3310.88 12.11 1469.0 57.97 1357.0 73272.0 332500.0 25.6 83.98 7.24 4.40 0.0 3.08 0.0 0.45 0.84 25.0 11.0 0.75 1.70 0.0 0.0 1.0 3478.0 3.13 1483.0 64.45 1491.0 75505.0 340800.0 27.5 83.09 5.95 3.62 0.14 4.97 0.06 2.01 0.14 24.0 18.0 1.21 1.62 0.0 0.0 1.0 3608.0 0.00 1524.0 71.19 1545.0 92207.0 351600.0 24.3 73.45 10.01 5.10 0.17 8.79 0.0 2.49 0.00 23.0 11.0 0.72 1.51 0.0 0.0 1.0 3608.0 0.00 1565.0 71.19 1545.0 92207.0 351600.0 24.3 73.45 10.01 5.10 0.17 8.79 0.0 2.49 0.00 22.0 7.0 0.45 1.41 0.0 0.0 1.0 3608.0 0.00 1606.0 71.19 1545.0 92207.0 351600.0 24.3 73.45 10.01 5.10 0.17 8.79 0.0 2.49 0.00 25.0 12.0 0.75 1.56 0.0 0.0 1.0 3608.0 0.00 1646.0 71.19 1545.0 92207.0 351600.0 24.3 73.45 10.01 5.10 0.17 8.79 0.0 2.49 0.00 26.0 12.0 0.73 1.58 0.0 0.0 1.0 3608.0 0.00 1687.0 71.19 1545.0 92207.0 351600.0 24.3 73.45 10.01 5.10 0.17 8.79 0.0 2.49 0.00 31.0 12.0 0.71 1.84 0.0 0.0 1.0 3608.0 0.00 1728.0 71.19 1545.0 92207.0 351600.0 24.3 73.45 10.01 5.10 0.17 8.79 0.0 2.49 0.00 25.0 16.0 0.93 1.45 0.0 0.0 1.0 MULTIPOLYGON (((487904.622 4422826.773, 487903...
436 42101000200 -75.1631 39.9529 -75.1511 39.9578 2 Philadelphia County, Pennsylvania 1362.00 56.42 374.0 81.48 421.0 8349.0 55600.0 31.2 11.16 5.21 1.69 0.07 79.59 0.07 2.20 0.00 NaN NaN NaN NaN 0.0 0.0 0.0 1362.00 56.42 415.0 81.48 421.0 8349.0 55600.0 31.2 11.16 5.21 1.69 0.07 79.59 0.07 2.20 0.00 NaN NaN NaN NaN 0.0 0.0 0.0 1362.00 56.42 455.0 81.48 421.0 8349.0 55600.0 31.2 11.16 5.21 1.69 0.07 79.59 0.07 2.20 0.00 4.0 4.0 0.88 0.88 1.0 0.0 0.0 1362.00 56.42 496.0 81.48 421.0 8349.0 55600.0 31.2 11.16 5.21 1.69 0.07 79.59 0.07 2.20 0.00 3.0 3.0 0.60 0.60 1.0 0.0 0.0 1362.00 56.42 537.0 81.48 421.0 8349.0 55600.0 31.2 11.16 5.21 1.69 0.07 79.59 0.07 2.20 0.00 6.0 6.0 1.12 1.12 1.0 0.0 0.0 1633.00 3.45 578.0 56.04 675.0 42083.0 232800.0 24.7 19.29 2.82 1.22 0.0 76.00 0.0 0.67 0.00 1.0 0.0 0.00 0.17 1.0 0.0 0.0 1633.00 3.45 618.0 56.04 675.0 42083.0 232800.0 24.7 19.29 2.82 1.22 0.0 76.00 0.0 0.67 0.00 6.0 6.0 0.97 0.97 1.0 0.0 0.0 1633.00 3.45 659.0 56.04 675.0 42083.0 232800.0 24.7 19.29 2.82 1.22 0.0 76.00 0.0 0.67 0.00 9.0 7.0 1.06 1.37 0.0 0.0 1.0 1633.00 3.45 700.0 56.04 675.0 42083.0 232800.0 24.7 19.29 2.82 1.22 0.0 76.00 0.0 0.67 0.00 11.0 7.0 1.00 1.57 0.0 0.0 1.0 1633.00 3.45 740.0 56.04 675.0 42083.0 232800.0 24.7 19.29 2.82 1.22 0.0 76.00 0.0 0.67 0.00 6.0 5.0 0.68 0.81 0.0 0.0 1.0 2937.0 5.07 781.0 68.21 905.0 49928.0 261100.0 26.4 22.64 9.67 2.69 0.10 63.16 0.03 1.40 0.31 6.0 1.0 0.13 0.77 0.0 0.0 1.0 2331.0 15.78 792.0 60.27 1263.0 59891.0 265400.0 28.1 38.91 6.05 1.54 0.47 50.75 0.0 2.27 0.00 9.0 6.0 0.76 1.14 0.0 0.0 1.0 2331.0 15.78 802.0 60.27 1263.0 59891.0 265400.0 28.1 38.91 6.05 1.54 0.47 50.75 0.0 2.27 0.00 8.0 3.0 0.37 1.00 0.0 0.0 1.0 2331.0 15.78 813.0 60.27 1263.0 59891.0 265400.0 28.1 38.91 6.05 1.54 0.47 50.75 0.0 2.27 0.00 14.0 10.0 1.23 1.72 0.0 0.0 1.0 2331.0 15.78 824.0 60.27 1263.0 59891.0 265400.0 28.1 38.91 6.05 1.54 0.47 50.75 0.0 2.27 0.00 5.0 3.0 0.36 0.61 0.0 0.0 1.0 2331.0 15.78 834.0 60.27 1263.0 59891.0 265400.0 28.1 38.91 6.05 1.54 0.47 50.75 0.0 2.27 0.00 10.0 9.0 1.08 1.20 0.0 0.0 1.0 2331.0 15.78 845.0 60.27 1263.0 59891.0 265400.0 28.1 38.91 6.05 1.54 0.47 50.75 0.0 2.27 0.00 11.0 8.0 0.95 1.30 0.0 0.0 1.0 MULTIPOLYGON (((487083.528 4422980.287, 487045...
437 42101000300 -75.1798 39.9544 -75.1623 39.9599 3 Philadelphia County, Pennsylvania 2570.00 12.16 861.0 69.49 688.0 40625.0 233900.0 29.0 70.86 14.67 3.81 0.27 7.00 0.08 3.04 0.27 NaN NaN NaN NaN 0.0 0.0 0.0 2570.00 12.16 915.0 69.49 688.0 40625.0 233900.0 29.0 70.86 14.67 3.81 0.27 7.00 0.08 3.04 0.27 NaN NaN NaN NaN 0.0 0.0 0.0 2570.00 12.16 969.0 69.49 688.0 40625.0 233900.0 29.0 70.86 14.67 3.81 0.27 7.00 0.08 3.04 0.27 14.0 12.0 1.24 1.44 1.0 0.0 0.0 2570.00 12.16 1023.0 69.49 688.0 40625.0 233900.0 29.0 70.86 14.67 3.81 0.27 7.00 0.08 3.04 0.27 21.0 17.0 1.66 2.05 1.0 0.0 0.0 2570.00 12.16 1077.0 69.49 688.0 40625.0 233900.0 29.0 70.86 14.67 3.81 0.27 7.00 0.08 3.04 0.27 23.0 23.0 2.13 2.13 1.0 0.0 0.0 4497.00 1.63 1132.0 65.66 1184.0 59189.0 438500.0 24.8 67.44 10.52 5.69 0.2 14.14 0.0 1.29 0.71 12.0 10.0 0.88 1.06 1.0 0.0 0.0 4497.00 1.63 1186.0 65.66 1184.0 59189.0 438500.0 24.8 67.44 10.52 5.69 0.2 14.14 0.0 1.29 0.71 19.0 16.0 1.35 1.60 1.0 0.0 0.0 4497.00 1.63 1240.0 65.66 1184.0 59189.0 438500.0 24.8 67.44 10.52 5.69 0.2 14.14 0.0 1.29 0.71 21.0 7.0 0.56 1.69 0.0 0.0 1.0 4497.00 1.63 1294.0 65.66 1184.0 59189.0 438500.0 24.8 67.44 10.52 5.69 0.2 14.14 0.0 1.29 0.71 25.0 11.0 0.85 1.93 0.0 0.0 1.0 4497.00 1.63 1348.0 65.66 1184.0 59189.0 438500.0 24.8 67.44 10.52 5.69 0.2 14.14 0.0 1.29 0.71 27.0 12.0 0.89 2.00 0.0 0.0 1.0 3169.0 7.20 1402.0 75.58 1827.0 71250.0 451800.0 28.0 72.26 10.22 4.26 0.03 10.35 0.03 2.52 0.32 24.0 13.0 0.93 1.71 0.0 0.0 1.0 3405.0 4.17 1489.0 70.47 1938.0 81950.0 469900.0 26.2 72.19 5.26 8.75 0.00 12.04 0.0 1.76 0.00 21.0 8.0 0.54 1.41 0.0 0.0 1.0 3405.0 4.17 1575.0 70.47 1938.0 81950.0 469900.0 26.2 72.19 5.26 8.75 0.00 12.04 0.0 1.76 0.00 27.0 12.0 0.76 1.71 0.0 0.0 1.0 3405.0 4.17 1662.0 70.47 1938.0 81950.0 469900.0 26.2 72.19 5.26 8.75 0.00 12.04 0.0 1.76 0.00 31.0 10.0 0.60 1.87 0.0 0.0 1.0 3405.0 4.17 1749.0 70.47 1938.0 81950.0 469900.0 26.2 72.19 5.26 8.75 0.00 12.04 0.0 1.76 0.00 27.0 14.0 0.80 1.54 0.0 0.0 1.0 3405.0 4.17 1835.0 70.47 1938.0 81950.0 469900.0 26.2 72.19 5.26 8.75 0.00 12.04 0.0 1.76 0.00 18.0 5.0 0.27 0.98 0.0 0.0 1.0 3405.0 4.17 1922.0 70.47 1938.0 81950.0 469900.0 26.2 72.19 5.26 8.75 0.00 12.04 0.0 1.76 0.00 26.0 14.0 0.73 1.35 0.0 0.0 1.0 MULTIPOLYGON (((486134.267 4423088.839, 486131...
438 42101000801 -75.1833 39.9486 -75.1773 39.9515 8.01 Philadelphia County, Pennsylvania 1478.00 14.40 810.0 73.65 933.0 42346.0 265200.0 27.6 81.67 2.97 3.50 0.04 10.08 0.04 1.26 0.45 NaN NaN NaN NaN 0.0 0.0 0.0 1478.00 14.40 801.0 73.65 933.0 42346.0 265200.0 27.6 81.67 2.97 3.50 0.04 10.08 0.04 1.26 0.45 NaN NaN NaN NaN 0.0 0.0 0.0 1478.00 14.40 793.0 73.65 933.0 42346.0 265200.0 27.6 81.67 2.97 3.50 0.04 10.08 0.04 1.26 0.45 7.0 5.0 0.63 0.88 1.0 0.0 0.0 1478.00 14.40 784.0 73.65 933.0 42346.0 265200.0 27.6 81.67 2.97 3.50 0.04 10.08 0.04 1.26 0.45 19.0 13.0 1.66 2.42 1.0 0.0 0.0 1478.00 14.40 775.0 73.65 933.0 42346.0 265200.0 27.6 81.67 2.97 3.50 0.04 10.08 0.04 1.26 0.45 17.0 14.0 1.81 2.19 1.0 0.0 0.0 1344.37 11.10 767.0 66.11 1393.0 61590.0 431800.0 28.2 86.58 1.05 3.14 0.0 8.16 0.0 0.88 0.18 10.0 6.0 0.78 1.30 1.0 0.0 0.0 1344.37 11.10 758.0 66.11 1393.0 61590.0 431800.0 28.2 86.58 1.05 3.14 0.0 8.16 0.0 0.88 0.18 12.0 7.0 0.92 1.58 1.0 0.0 0.0 1344.37 11.10 749.0 66.11 1393.0 61590.0 431800.0 28.2 86.58 1.05 3.14 0.0 8.16 0.0 0.88 0.18 12.0 5.0 0.67 1.60 0.0 0.0 1.0 1344.37 11.10 740.0 66.11 1393.0 61590.0 431800.0 28.2 86.58 1.05 3.14 0.0 8.16 0.0 0.88 0.18 11.0 4.0 0.54 1.49 0.0 0.0 1.0 1344.37 11.10 732.0 66.11 1393.0 61590.0 431800.0 28.2 86.58 1.05 3.14 0.0 8.16 0.0 0.88 0.18 10.0 2.0 0.27 1.37 0.0 0.0 1.0 1562.0 2.46 723.0 71.09 2001.0 83125.0 459900.0 25.9 78.04 2.94 5.76 0.00 10.82 0.26 1.92 0.26 14.0 4.0 0.55 1.94 0.0 0.0 1.0 1692.0 3.25 734.0 74.87 2219.0 81620.0 656300.0 24.7 75.18 10.64 4.91 0.00 4.08 0.0 1.42 3.78 13.0 7.0 0.95 1.77 0.0 0.0 1.0 1692.0 3.25 746.0 74.87 2219.0 81620.0 656300.0 24.7 75.18 10.64 4.91 0.00 4.08 0.0 1.42 3.78 7.0 0.0 0.00 0.94 0.0 0.0 1.0 1692.0 3.25 757.0 74.87 2219.0 81620.0 656300.0 24.7 75.18 10.64 4.91 0.00 4.08 0.0 1.42 3.78 15.0 3.0 0.40 1.98 0.0 0.0 1.0 1692.0 3.25 768.0 74.87 2219.0 81620.0 656300.0 24.7 75.18 10.64 4.91 0.00 4.08 0.0 1.42 3.78 10.0 4.0 0.52 1.30 0.0 0.0 1.0 1692.0 3.25 780.0 74.87 2219.0 81620.0 656300.0 24.7 75.18 10.64 4.91 0.00 4.08 0.0 1.42 3.78 16.0 8.0 1.03 2.05 0.0 0.0 1.0 1692.0 3.25 791.0 74.87 2219.0 81620.0 656300.0 24.7 75.18 10.64 4.91 0.00 4.08 0.0 1.42 3.78 13.0 4.0 0.51 1.64 0.0 0.0 1.0 MULTIPOLYGON (((484852.936 4422329.776, 484807...
439 42101000804 -75.1712 39.9470 -75.1643 39.9501 8.04 Philadelphia County, Pennsylvania 3301.00 14.40 2058.0 73.65 933.0 42346.0 265200.0 27.6 81.67 2.97 3.50 0.04 10.08 0.04 1.26 0.45 NaN NaN NaN NaN 0.0 0.0 0.0 3301.00 14.40 2050.0 73.65 933.0 42346.0 265200.0 27.6 81.67 2.97 3.50 0.04 10.08 0.04 1.26 0.45 NaN NaN NaN NaN 0.0 0.0 0.0 3301.00 14.40 2042.0 73.65 933.0 42346.0 265200.0 27.6 81.67 2.97 3.50 0.04 10.08 0.04 1.26 0.45 22.0 18.0 0.88 1.08 1.0 0.0 0.0 3301.00 14.40 2033.0 73.65 933.0 42346.0 265200.0 27.6 81.67 2.97 3.50 0.04 10.08 0.04 1.26 0.45 31.0 21.0 1.03 1.52 1.0 0.0 0.0 3301.00 14.40 2025.0 73.65 933.0 42346.0 265200.0 27.6 81.67 2.97 3.50 0.04 10.08 0.04 1.26 0.45 18.0 15.0 0.74 0.89 1.0 0.0 0.0 3002.54 11.10 2017.0 66.11 1393.0 61590.0 431800.0 28.2 86.58 1.05 3.14 0.0 8.16 0.0 0.88 0.18 28.0 19.0 0.94 1.39 1.0 0.0 0.0 3002.54 11.10 2009.0 66.11 1393.0 61590.0 431800.0 28.2 86.58 1.05 3.14 0.0 8.16 0.0 0.88 0.18 14.0 13.0 0.65 0.70 1.0 0.0 0.0 3002.54 11.10 2001.0 66.11 1393.0 61590.0 431800.0 28.2 86.58 1.05 3.14 0.0 8.16 0.0 0.88 0.18 33.0 11.0 0.55 1.65 0.0 0.0 1.0 3002.54 11.10 1992.0 66.11 1393.0 61590.0 431800.0 28.2 86.58 1.05 3.14 0.0 8.16 0.0 0.88 0.18 17.0 4.0 0.20 0.85 0.0 0.0 1.0 3002.54 11.10 1984.0 66.11 1393.0 61590.0 431800.0 28.2 86.58 1.05 3.14 0.0 8.16 0.0 0.88 0.18 27.0 8.0 0.40 1.36 0.0 0.0 1.0 3609.0 7.69 1976.0 76.32 1562.0 75357.0 330200.0 26.0 78.55 2.72 4.96 0.03 11.75 0.03 1.72 0.25 43.0 13.0 0.66 2.18 0.0 0.0 1.0 3746.0 0.00 2000.0 75.43 1816.0 96250.0 465500.0 23.7 67.43 4.51 13.59 0.35 13.59 0.0 0.19 0.35 38.0 9.0 0.45 1.90 0.0 0.0 1.0 3746.0 0.00 2024.0 75.43 1816.0 96250.0 465500.0 23.7 67.43 4.51 13.59 0.35 13.59 0.0 0.19 0.35 31.0 16.0 0.79 1.53 0.0 0.0 1.0 3746.0 0.00 2048.0 75.43 1816.0 96250.0 465500.0 23.7 67.43 4.51 13.59 0.35 13.59 0.0 0.19 0.35 27.0 8.0 0.39 1.32 0.0 0.0 1.0 3746.0 0.00 2072.0 75.43 1816.0 96250.0 465500.0 23.7 67.43 4.51 13.59 0.35 13.59 0.0 0.19 0.35 28.0 11.0 0.53 1.35 0.0 0.0 1.0 3746.0 0.00 2096.0 75.43 1816.0 96250.0 465500.0 23.7 67.43 4.51 13.59 0.35 13.59 0.0 0.19 0.35 18.0 7.0 0.33 0.86 0.0 0.0 1.0 3746.0 0.00 2120.0 75.43 1816.0 96250.0 465500.0 23.7 67.43 4.51 13.59 0.35 13.59 0.0 0.19 0.35 22.0 7.0 0.33 1.04 0.0 0.0 1.0 MULTIPOLYGON (((485376.999 4421975.806, 485390...

1.1.3 Transform from wide to tidy format¶

For this assignment, we are interested in the number of evictions by census tract for various years. Right now, each year has it's own column, so it will be easiest to transform to a tidy format.

Use the pd.melt() function to transform the eviction data into tidy format, using the number of evictions from 2003 to 2016.

The tidy data frame should have four columns: GEOID, geometry, a column holding the number of evictions, and a column telling you what the name of the original column was for that value.

Hints:

  • You'll want to specify the GEOID and geometry columns as the id_vars. This will keep track of the census tract information.
  • You should specify the names of the columns holding the number of evictions as the value_vars.
  • You can generate a list of this column names using Python's f-string formatting:
    value_vars = [f"e-{x:02d}" for x in range(3, 17)]
In [5]:
 value_vars = [f"e-{x:02d}" for x in range(3, 17)]
phltracts = pd.melt(
    frame=phltracts,
    id_vars=['GEOID', 'geometry'],
    value_vars=value_vars,
    var_name='original_column_name',
    value_name='number_of_evictions',
)
phltracts.head()
Out[5]:
GEOID geometry original_column_name number_of_evictions
0 42101000100 MULTIPOLYGON (((487904.622 4422826.773, 487903... e-03 21.0
1 42101000200 MULTIPOLYGON (((487083.528 4422980.287, 487045... e-03 3.0
2 42101000300 MULTIPOLYGON (((486134.267 4423088.839, 486131... e-03 17.0
3 42101000801 MULTIPOLYGON (((484852.936 4422329.776, 484807... e-03 13.0
4 42101000804 MULTIPOLYGON (((485376.999 4421975.806, 485390... e-03 21.0
In [6]:
phltracts['original_column_name'] = phltracts['original_column_name'].str.extract(r'e-(\d+)').apply(lambda x: '20' + x)
phltracts.rename(columns={'original_column_name': 'year'}, inplace=True)
phltracts.head()
Out[6]:
GEOID geometry year number_of_evictions
0 42101000100 MULTIPOLYGON (((487904.622 4422826.773, 487903... 2003 21.0
1 42101000200 MULTIPOLYGON (((487083.528 4422980.287, 487045... 2003 3.0
2 42101000300 MULTIPOLYGON (((486134.267 4423088.839, 486131... 2003 17.0
3 42101000801 MULTIPOLYGON (((484852.936 4422329.776, 484807... 2003 13.0
4 42101000804 MULTIPOLYGON (((485376.999 4421975.806, 485390... 2003 21.0

1.1.4 Plot the total number of evictions per year from 2003 to 2016¶

Use hvplot to plot the total number of evictions from 2003 to 2016. You will first need to perform a group by operation and sum up the total number of evictions for all census tracts, and then use hvplot() to make your plot.

You can use any type of hvplot chart you'd like to show the trend in number of evictions over time.

In [7]:
evictionsbyyear = phltracts.groupby('year')['number_of_evictions'].sum().reset_index()

plot1 = evictionsbyyear.hvplot.line(x='year', y='number_of_evictions', xlabel='Year', ylabel='Total Evictions',
                                           title='Total Evictions by Year')

plot1
Out[7]:

1.1.5 The number of evictions across Philadelphia¶

Our tidy data frame is still a GeoDataFrame with a geometry column, so we can visualize the number of evictions for all census tracts.

Use hvplot() to generate a choropleth showing the number of evictions for a specified year, with a widget dropdown to select a given year (or variable name, e.g., e-16, e-15, etc).

Hints

  • You'll need to use the groupby keyword to tell hvplot to make a series of maps, with a widget to select between them.
  • You will need to specify dynamic=False as a keyword argument to the hvplot() function.
  • Be sure to specify a width and height that makes your output map (roughly) square to limit distortions
In [8]:
phltracts=phltracts.to_crs("EPSG:32618")
In [9]:
evictions_map = phltracts.hvplot(
    c="number_of_evictions",
    frame_width=600,
    frame_height=600,
    geo=True,
    crs=32618,
    cmap="viridis",
    dynamic=False,
    groupby='year',  # Use the 'year' column for grouping
)

evictions_map
Out[9]:

1.2 Code Violations in Philadelphia¶

Next, we'll explore data for code violations from the Licenses and Inspections Department of Philadelphia to look for potential correlations with the number of evictions.

1.2.1 Load data from 2012 to 2016¶

L+I violation data for years including 2012 through 2016 (inclusive) is provided in a CSV format in the "data/" folder.

Load the data using pandas and convert to a GeoDataFrame.

In [10]:
codeviolations=pd.read_csv('li_violations.csv')
In [11]:
codeviolations = gpd.GeoDataFrame(codeviolations, geometry=gpd.points_from_xy(codeviolations.lng, codeviolations.lat), crs="EPSG:32618")
codeviolations.head()
Out[11]:
lat lng violationdescription geometry
0 40.050526 -75.126076 CLIP VIOLATION NOTICE POINT (-75.126 40.051)
1 40.050593 -75.126578 LICENSE-CHANGE OF ADDRESS POINT (-75.127 40.051)
2 40.050593 -75.126578 LICENSE-RES SFD/2FD POINT (-75.127 40.051)
3 39.991994 -75.128895 EXT A-CLEAN WEEDS/PLANTS POINT (-75.129 39.992)
4 40.023260 -75.164848 EXT A-VACANT LOT CLEAN/MAINTAI POINT (-75.165 40.023)

1.2.2 Trim to specific violation types¶

There are many different types of code violations (running the nunique() function on the violationdescription column will extract all of the unique ones). More information on different types of violations can be found on the City's website.

Below, I've selected 15 types of violations that deal with property maintenance and licensing issues. We'll focus on these violations. The goal is to see if these kinds of violations are correlated spatially with the number of evictions in a given area.

Use the list of violations given to trim your data set to only include these types.

In [12]:
violation_types = [
    "INT-PLMBG MAINT FIXTURES-RES",
    "INT S-CEILING REPAIR/MAINT SAN",
    "PLUMBING SYSTEMS-GENERAL",
    "CO DETECTOR NEEDED",
    "INTERIOR SURFACES",
    "EXT S-ROOF REPAIR",
    "ELEC-RECEPTABLE DEFECTIVE-RES",
    "INT S-FLOOR REPAIR",
    "DRAINAGE-MAIN DRAIN REPAIR-RES",
    "DRAINAGE-DOWNSPOUT REPR/REPLC",
    "LIGHT FIXTURE DEFECTIVE-RES",
    "LICENSE-RES SFD/2FD",
    "ELECTRICAL -HAZARD",
    "VACANT PROPERTIES-GENERAL",
    "INT-PLMBG FIXTURES-RES",
]
In [13]:
filteredCodeViolations = codeviolations[codeviolations['violationdescription'].isin(violation_types)]
In [14]:
filteredCodeViolations.crs = "EPSG:32618"
phltracts = phltracts.to_crs("EPSG:32618")
In [ ]:
In [15]:
censustracts = gpd.read_file('Census_Tracts_2010.geojson')
censustracts.crs = "EPSG:32618"
censustracts = censustracts[['GEOID10', 'geometry']]
censustracts.head()
Out[15]:
GEOID10 geometry
0 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75....
1 42101009500 POLYGON ((-75.235 39.969, -75.235 39.969, -75....
2 42101009600 POLYGON ((-75.243 39.962, -75.243 39.962, -75....
3 42101013800 POLYGON ((-75.173 39.978, -75.174 39.978, -75....
4 42101013900 POLYGON ((-75.173 39.978, -75.173 39.977, -75....

1.2.3 Make a hex bin map¶

The code violation data is point data. We can get a quick look at the geographic distribution using matplotlib and the hexbin() function. Make a hex bin map of the code violations and overlay the census tract outlines.

Hints:

  • The eviction data from part 1 was by census tract, so the census tract geometries are available as part of that GeoDataFrame. You can use it to overlay the census tracts on your hex bin map.
  • Make sure you convert your GeoDataFrame to a CRS that's better for visualization than plain old 4326.
In [16]:
fig, ax = plt.subplots(figsize=(10, 10))

xcoords = filteredCodeViolations.geometry.x
ycoords = filteredCodeViolations.geometry.y
ax.hexbin(xcoords, ycoords, gridsize=100)
ax.set_xlim(xcoords.min(), xcoords.max())
ax.set_ylim(ycoords.min(), ycoords.max())

censustracts.boundary.plot(
    ax=ax, facecolor="none", linewidth=0.5, edgecolor="white"
)

ax.set_axis_off()
ax.set_aspect("equal")
plt.show()
No description has been provided for this image

1.2.4 Spatially join data sets¶

To do a census tract comparison to our eviction data, we need to find which census tract each of the code violations falls into. Use the geopandas.sjoin() function to do just that.

Hints

  • You can re-use your eviction data frame, but you will only need the geometry column (specifying census tract polygons) and the GEOID column (specifying the name of each census tract).
  • Make sure both data frames have the same CRS before joining them together!
In [17]:
CodeViolations = gpd.sjoin(filteredCodeViolations, censustracts, predicate = "within", how="right")
CodeViolations.head()
Out[17]:
index_left lat lng violationdescription GEOID10 geometry
0 77810.0 39.961258 -75.232943 INT S-CEILING REPAIR/MAINT SAN 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75....
0 403958.0 39.961258 -75.232943 INT S-CEILING REPAIR/MAINT SAN 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75....
0 349005.0 39.961359 -75.233767 LICENSE-RES SFD/2FD 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75....
0 392549.0 39.961539 -75.233879 INT S-CEILING REPAIR/MAINT SAN 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75....
0 344387.0 39.961672 -75.233248 INT S-FLOOR REPAIR 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75....

1.2.5 Calculate the number of violations by type per census tract¶

Next, we'll want to find the number of violations (for each kind) per census tract. You should group the data frame by violation type and census tract name.

The result of this step should be a data frame with three columns: violationdescription, GEOID, and N, where N is the number of violations of that kind in the specified census tract.

Optional: to make prettier plots

Some census tracts won't have any violations, and they won't be included when we do the above calculation. However, there is a trick to set the values for those census tracts to be zero. After you calculate the sizes of each violation/census tract group, you can run:

N = N.unstack(fill_value=0).stack().reset_index(name='N')

where N gives the total size of each of the groups, specified by violation type and census tract name.

See this StackOverflow post for more details.

This part is optional, but will make the resulting maps a bit prettier.

In [18]:
grouped_CV = CodeViolations.groupby(['GEOID10', 'violationdescription']).size().reset_index(name='count')
merged_CV = gpd.GeoDataFrame(pd.merge(censustracts, grouped_CV, on='GEOID10'))
merged_CV
Out[18]:
GEOID10 geometry violationdescription count
0 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75.... CO DETECTOR NEEDED 33
1 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75.... DRAINAGE-DOWNSPOUT REPR/REPLC 16
2 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75.... DRAINAGE-MAIN DRAIN REPAIR-RES 11
3 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75.... ELEC-RECEPTABLE DEFECTIVE-RES 11
4 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75.... EXT S-ROOF REPAIR 31
... ... ... ... ...
3989 42101037700 POLYGON ((-75.152 39.986, -75.152 39.986, -75.... INT-PLMBG MAINT FIXTURES-RES 2
3990 42101037700 POLYGON ((-75.152 39.986, -75.152 39.986, -75.... INTERIOR SURFACES 3
3991 42101037700 POLYGON ((-75.152 39.986, -75.152 39.986, -75.... LICENSE-RES SFD/2FD 9
3992 42101037700 POLYGON ((-75.152 39.986, -75.152 39.986, -75.... LIGHT FIXTURE DEFECTIVE-RES 3
3993 42101037700 POLYGON ((-75.152 39.986, -75.152 39.986, -75.... PLUMBING SYSTEMS-GENERAL 1

3994 rows × 4 columns

In [19]:
merged_CV = merged_CV.to_crs('EPSG:32618')

1.2.6 Merge with census tracts geometries¶

We now have the number of violations of different types per census tract specified as a regular DataFrame. You can now merge it with the census tract geometries (from your eviction data GeoDataFrame) to create a GeoDataFrame.

Hints

  • Use pandas.merge() and specify the on keyword to be the column holding census tract names.
  • Make sure the result of the merge operation is a GeoDataFrame — you will want the GeoDataFrame holding census tract geometries to be the first argument of the pandas.merge() function.

1.2.7 Interactive choropleths for each violation type¶

Now, we can use hvplot() to create an interactive choropleth for each violation type and add a widget to specify different violation types.

Hints

  • You'll need to use the groupby keyword to tell hvplot to make a series of maps, with a widget to select different violation types.
  • You will need to specify dynamic=False as a keyword argument to the hvplot() function.
  • Be sure to specify a width and height that makes your output map (roughly) square to limit distortions
In [20]:
violations_map = merged_CV.hvplot(
    c='count',
    frame_width=600,
    frame_height=600,
    geo=True,
    cmap="viridis",
    dynamic=False,
    groupby='violationdescription'
)
violations_map
Out[20]:

1.3. A side-by-side comparison¶

From the interactive maps of evictions and violations, you should notice a lot of spatial overlap.

As a final step, we'll make a side-by-side comparison to better show the spatial correlations. This will involve a few steps:

  1. Trim the evictions data frame plotted in section 1.1.5 to only include evictions from 2016.
  2. Trim the L+I violations data frame plotted in section 1.2.7 to only include a single violation type (pick whichever one you want!).
  3. Use hvplot() to make two interactive choropleth maps, one for the data from step 1. and one for the data in step 2.
  4. Show these two plots side by side (one row and 2 columns) using the syntax for combining charts.

Note: since we selected a single year and violation type, you won't need to use the groupby= keyword here.

In [21]:
violation = (merged_CV['violationdescription'] == "CO DETECTOR NEEDED")
merged_CV = merged_CV[violation]
merged_CV
Out[21]:
GEOID10 geometry violationdescription count
0 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75.... CO DETECTOR NEEDED 33
14 42101009500 POLYGON ((-75.235 39.969, -75.235 39.969, -75.... CO DETECTOR NEEDED 23
28 42101009600 POLYGON ((-75.243 39.962, -75.243 39.962, -75.... CO DETECTOR NEEDED 40
43 42101013800 POLYGON ((-75.173 39.978, -75.174 39.978, -75.... CO DETECTOR NEEDED 8
54 42101013900 POLYGON ((-75.173 39.978, -75.173 39.977, -75.... CO DETECTOR NEEDED 5
... ... ... ... ...
3932 42101037200 POLYGON ((-75.171 39.917, -75.171 39.917, -75.... CO DETECTOR NEEDED 6
3940 42101038300 POLYGON ((-75.116 40.017, -75.117 40.017, -75.... CO DETECTOR NEEDED 21
3955 42101039000 POLYGON ((-75.088 40.040, -75.088 40.041, -75.... CO DETECTOR NEEDED 28
3969 42101037800 POLYGON ((-75.111 39.970, -75.107 39.971, -75.... CO DETECTOR NEEDED 7
3981 42101037700 POLYGON ((-75.152 39.986, -75.152 39.986, -75.... CO DETECTOR NEEDED 11

325 rows × 4 columns

In [22]:
censustracts = gpd.read_file('Census_Tracts_2010.geojson')
censustracts.crs = "EPSG:32618"
censustracts = censustracts[['GEOID10', 'geometry']]
censustracts.rename(columns={'GEOID10': 'GEOID'}, inplace=True)
censustracts.head()
Out[22]:
GEOID geometry
0 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75....
1 42101009500 POLYGON ((-75.235 39.969, -75.235 39.969, -75....
2 42101009600 POLYGON ((-75.243 39.962, -75.243 39.962, -75....
3 42101013800 POLYGON ((-75.173 39.978, -75.174 39.978, -75....
4 42101013900 POLYGON ((-75.173 39.978, -75.173 39.977, -75....
In [23]:
phltracts16 = phltracts[phltracts['year'] == "2016"]
phltracts16 = phltracts16[['GEOID', 'number_of_evictions', 'year']]
phltracts16 = censustracts.merge(phltracts16, on='GEOID')
phltracts16 = gpd.GeoDataFrame(phltracts16, geometry = 'geometry')
phltracts16 = phltracts16.to_crs('EPSG:32618')
phltracts16.head()
Out[23]:
GEOID geometry number_of_evictions year
0 42101009400 POLYGON ((-75.229 39.961, -75.229 39.960, -75.... 48.0 2016
1 42101009500 POLYGON ((-75.235 39.969, -75.235 39.969, -75.... 36.0 2016
2 42101009600 POLYGON ((-75.243 39.962, -75.243 39.962, -75.... 51.0 2016
3 42101013800 POLYGON ((-75.173 39.978, -75.174 39.978, -75.... 12.0 2016
4 42101013900 POLYGON ((-75.173 39.978, -75.173 39.977, -75.... 27.0 2016
In [24]:
violations = merged_CV.hvplot(
    c='count',
    geo=True,
    crs='32618',
    cmap="viridis",
    title= "Co Detector Needed Code Violations",
    frame_width=400,
    frame_height=400,
    dynamic=False,
)

evictions = phltracts16.hvplot(
    c="number_of_evictions",
    geo=True,
    crs='32618',
    cmap="viridis",
    title= "Evictions in 2016",
    frame_width=400,
    frame_height=400,
    dynamic=False,
)

combined_plots = pn.Row(evictions, violations)

combined_plots

C:\Users\cruse\mambaforge1\envs\musa-550-fall-2023\lib\site-packages\osgeo\osr.py:385: FutureWarning: Neither osr.UseExceptions() nor osr.DontUseExceptions() has been explicitly called. In GDAL 4.0, exceptions will be enabled by default.
  warnings.warn(
Out[24]:

Part 2: Exploring the NDVI in Philadelphia¶

In this part, we'll explore the NDVI in Philadelphia a bit more. This part will include two parts:

  1. We'll compare the median NDVI within the city limits and the immediate suburbs
  2. We'll calculate the NDVI around street trees in the city.

2.1 Comparing the NDVI in the city and the suburbs¶

2.1.1 Load Landsat data for Philadelphia¶

Use rasterio to load the landsat data for Philadelphia (available in the "data/" folder)

In [25]:
import rasterio as rio
landsat = rio.open("landsat8_philly.tif")
In [26]:
phlbounds = gpd.read_file("City_Limits.geojson")
phlbounds = phlbounds.to_crs('EPSG:32618')

2.1.2 Separating the city from the suburbs¶

Create two polygon objects, one for the city limits and one for the suburbs. To calculate the suburbs polygon, we will take everything outside the city limits but still within the bounding box.

  • The city limits are available in the "data/" folder.
  • To calculate the suburbs polygon, the "envelope" attribute of the city limits geometry will be useful.
  • You can use geopandas' geometric manipulation functionality to calculate the suburbs polygon from the city limits polygon and the envelope polygon.
In [27]:
landsat.bounds
Out[27]:
BoundingBox(left=476064.3596176505, bottom=4413096.927074196, right=503754.3596176505, top=4443066.927074196)
In [28]:
from shapely.geometry import box
from shapely.geometry import box, Polygon
bounding_box_polygon = box(476064.3596176505, 4413096.927074196, 503754.3596176505, 4443066.927074196) 
bounding_box = gpd.GeoDataFrame(geometry=[bounding_box_polygon], crs = 'EPSG:32618')
In [29]:
suburbs = bounding_box.difference(phlbounds)

2.1.3 Mask and calculate the NDVI for the city and the suburbs¶

Using the two polygons from the last section, use rasterio's mask functionality to create two masked arrays from the landsat data, one for the city and one for the suburbs.

For each masked array, calculate the NDVI.

In [30]:
landsat_extent = [
    landsat.bounds.left,
    landsat.bounds.right,
    landsat.bounds.bottom,
    landsat.bounds.top,
]
In [31]:
from rasterio import mask
city, mask_transform = mask.mask(
    dataset=landsat,              
    shapes=phlbounds.geometry,   
    crop=True,                   
    all_touched=True,            
    filled=False,               
)

suburb, mask_transform = mask.mask(
    dataset=landsat,              
    shapes=suburbs.geometry,   
    crop=True,                   
    all_touched=True,            
    filled=False,               
)
In [32]:
red = city[3]
nir = city[4]
red.mask
Out[32]:
array([[ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True],
       ...,
       [ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True]])
In [33]:
check = np.logical_and(red.mask == False, nir.mask == False)
check
Out[33]:
array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]])
In [34]:
def calculate_NDVI(nir, red):
    nir = nir.astype(float)
    red = red.astype(float)
    check = np.logical_and(red.mask == False, nir.mask == False)

    ndvi = np.where(check, (nir - red) / (nir + red), np.nan)
    return ndvi
In [35]:
cityNDVI = calculate_NDVI(nir, red)
cityNDVI
Out[35]:
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]])
In [36]:
fig, ax = plt.subplots(figsize=(10, 10))

# Plot NDVI
img = ax.imshow(cityNDVI, extent=landsat_extent)

# Format and plot city limits
phlbounds.plot(ax=ax, edgecolor="gray", facecolor="none", linewidth=4)
plt.colorbar(img)
ax.set_axis_off()
ax.set_title("NDVI in Philadelphia", fontsize=18);
No description has been provided for this image
In [37]:
red1 = suburb[3]
nir1 = suburb[4]
red.mask
suburbNDVI = calculate_NDVI(nir1, red1)
suburbNDVI
Out[37]:
array([[0.37784607, 0.43111497, 0.46573645, ..., 0.05923377, 0.17133027,
        0.08378433],
       [0.3493712 , 0.43525381, 0.47612206, ..., 0.05134107, 0.07925549,
        0.10214586],
       [0.36523481, 0.44101499, 0.48373722, ..., 0.09906358, 0.07697141,
        0.06734949],
       ...,
       [0.28478623, 0.27036395, 0.26653252, ..., 0.23627685, 0.18276192,
        0.30369115],
       [0.18844363, 0.23383268, 0.31798551, ..., 0.23022903, 0.19907662,
        0.36404772],
       [0.27879048, 0.30900491, 0.42362817, ..., 0.33538113, 0.19557689,
        0.20141482]])
In [38]:
fig, ax = plt.subplots(figsize=(10, 10))

# Plot NDVI
img = ax.imshow(suburbNDVI, extent=landsat_extent)

# Format and plot city limits
phlbounds.plot(ax=ax, edgecolor="gray", facecolor="none", linewidth=4)
plt.colorbar(img)
ax.set_axis_off()
ax.set_title("NDVI in Suburbs", fontsize=18);
No description has been provided for this image

2.1.4 Calculate the median NDVI within the city and within the suburbs¶

  • Calculate the median value from your NDVI arrays for the city and suburbs
  • Numpy's nanmedian function will be useful for ignoring NaN elements
  • Print out the median values. Which has a higher NDVI: the city or suburbs?
In [39]:
median_cityNDVI = np.nanmedian(cityNDVI)
median_cityNDVI
Out[39]:
0.20268593532493442
In [40]:
median_suburbNDVI = np.nanmedian(suburbNDVI)
median_suburbNDVI
Out[40]:
0.3746654463028859

2.2 Calculating the NDVI for Philadelphia's street treets¶

2.2.1 Load the street tree data¶

The data is available in the "data/" folder. It has been downloaded from OpenDataPhilly. It contains the locations of abot 2,500 street trees in Philadelphia.

In [41]:
streettrees = gpd.read_file('ppr_tree_canopy_points_2015.geojson')
streettrees = streettrees.to_crs('32618')
streettrees.crs
Out[41]:
<Projected CRS: EPSG:32618>
Name: WGS 84 / UTM zone 18N
Axis Info [cartesian]:
- E[east]: Easting (metre)
- N[north]: Northing (metre)
Area of Use:
- name: Between 78°W and 72°W, northern hemisphere between equator and 84°N, onshore and offshore. Bahamas. Canada - Nunavut; Ontario; Quebec. Colombia. Cuba. Ecuador. Greenland. Haiti. Jamaica. Panama. Turks and Caicos Islands. United States (USA). Venezuela.
- bounds: (-78.0, 0.0, -72.0, 84.0)
Coordinate Operation:
- name: UTM zone 18N
- method: Transverse Mercator
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

2.2.2 Calculate the NDVI values at the locations of the street trees¶

  • Use the rasterstats package to calculate the NDVI values at the locations of the street trees.
  • Since these are point geometries, you can calculate either the median or the mean statistic (only one pixel will contain each point).
In [42]:
from rasterstats import zonal_stats

mean_stats = zonal_stats(
    streettrees, 
    cityNDVI, 
    affine=landsat.transform,  
    stats=["mean"],  
    nodata=np.nan, 
)

2.2.3 Plotting the results¶

Make two plots of the results:

  1. A histogram of the NDVI values, using matplotlib's hist function. Include a vertical line that marks the NDVI = 0 threshold
  2. A plot of the street tree points, colored by the NDVI value, using geopandas' plot function. Include the city limits boundary on your plot.

The figures should be clear and well-styled, with for example, labels for axes, legends, and clear color choices.

In [43]:
meanNDVI = [stats_dict["mean"] for stats_dict in mean_stats]
streettrees["meanNDVI"] = meanNDVI
streettrees.head()
Out[43]:
objectid fcode geometry meanNDVI
0 1 3000 POINT (499541.269 4434698.265) 0.235337
1 2 3000 POINT (488932.471 4424093.158) 0.261535
2 3 3000 POINT (489039.214 4425985.827) 0.096769
3 4 3000 POINT (488993.171 4426088.005) 0.076630
4 5 3000 POINT (488943.113 4424599.478) 0.267952
In [44]:
fig, ax = plt.subplots(figsize=(8, 6))

ax.hist(streettrees["meanNDVI"], bins="auto")
ax.axvline(x=0, c="k", lw=2)

ax.set_xlabel("Mean NDVI", fontsize=18)
ax.set_ylabel("Number of Street Trees", fontsize=18)
Out[44]:
Text(0, 0.5, 'Number of Street Trees')
No description has been provided for this image
In [45]:
fig, ax = plt.subplots(figsize=(10, 10))

# Plot NDVI
img = ax.imshow(cityNDVI, extent=landsat_extent)

# Add the city limits
phlbounds.plot(ax=ax, edgecolor="gray", facecolor="none", linewidth=4)

# NEW: add the parks
streettrees.plot(ax=ax, color = "yellow")

# Format and add colorbar
plt.colorbar(img)
ax.set_axis_off()
ax.set_title("NDVI vs. Street Trees in Philadelphia", fontsize=18);
No description has been provided for this image
In [ ]: